A novel image analysis algorithm reveals that media conditioned with chitosan and platelet-rich plasma biomaterial dose dependently increases fibroblast migration in a scratch assay

Ahunon, Laura; Milano, Fiona; Chevrier, Anik; Lavertu, Marc

doi:10.1088/2057-1976/abbe72

Cited by 2 publications

(2 citation statements)

References 43 publications

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“…However, toolboxes currently in use in the literature rely on assumptions about cell behavior and image processing operations, that might be prone to errors, take a long time, and lack reproducibility due to their reliance on manual intervention [12]. The commonly used toolbox, ImageJ has several limitations such as the inability to separate cells, the conversion of branched cells into circular shapes, and the cells that were smaller than the radius value specified by the user were not considered cells [36]. For the white-wave model to be effective, experiment-long fixed images must be acquired, and collecting them manually exposes the application to the potential for errors [15].…”

Section: Evaluating Implicationsmentioning

confidence: 99%

An automated in vitro wound healing microscopy image analysis approach utilizing U-net-based deep learning methodology

Dogru,

Ozdemir,

Ozdemir

et al. 2024

Preprint

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Background: The assessment of in vitro wound healing images is critical for determining the efficacy of the therapy-of-interest that may influence the wound healing process. Existing methods suffer significant limitations, such as their user dependency and time-consuming nature, and lack of sensitivity, and thus pave the way for automated analysis approaches. Methods: Hereby, three structurally different variations of U-net architectures based on convolutional neural networks (CNN) were implemented for the segmentation of in vitro wound healing microscopy images. Developed models were fed using two independent datasets after applying a novel augmentation method based aim at the more sensitive analysis of edges after the preprocessing. Then, predicted masks were utilized for the accurate calculation of wound areas. Eventually, the therapy efficacy-indicator wound areas were thoroughly compared with current well-known tools such as ImageJ and TScratch. Results: The average dice similarity coefficient (DSC) scores were obtained as 0.958∼0.968 for U-net-based deep learning models. The averaged absolute percentage errors of predicted wound areas to ground truth were 6.41%, 3.70%, and 3.73%, respectively for U-net, U-net++, and Attention U-net, while ImageJ and TScratch had huge averaged error rates of 22.59% and 33.88%, respectively. Conclusions: Comparative analyses revealed that the developed models outperformed the conventional approaches in terms of analysis time and segmentation sensitivity. The developed models hold also great promise for the prediction of the in vitro wound area regardless of the therapy-of-interest, cell line, magnification of the microscope, or other application-dependent parameters.

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Section: Evaluating Implicationsmentioning

confidence: 99%

An automated in vitro wound healing microscopy image analysis approach utilizing U-net-based deep learning methodology

Dogru,

Ozdemir,

Ozdemir

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…However, toolboxes currently in use in the literature rely on assumptions about cell behavior and image processing operations that might be prone to errors, take a long time, and lack reproducibility due to their reliance on manual intervention [15]. The commonly used toolbox ImageJ has several limitations, such as the inability to separate cells, the conversion of the cells in spindle movement into circular shapes, and the fact that cells that were smaller than the radius value specified by the user were not considered cells [56]. For the white-wave model to be effective, experiment-long fixed images must be acquired, and collecting them manually exposes the application to the potential for errors [20].…”

Section: Evaluating Implicationsmentioning

confidence: 99%

An automated in vitro wound healing microscopy image analysis approach utilizing U-net-based deep learning methodology

Doğru,

Özdemir,

Özdemir

et al. 2024

BMC Med Imaging

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Background The assessment of in vitro wound healing images is critical for determining the efficacy of the therapy-of-interest that may influence the wound healing process. Existing methods suffer significant limitations, such as user dependency, time-consuming nature, and lack of sensitivity, thus paving the way for automated analysis approaches. Methods Hereby, three structurally different variations of U-net architectures based on convolutional neural networks (CNN) were implemented for the segmentation of in vitro wound healing microscopy images. The developed models were fed using two independent datasets after applying a novel augmentation method aimed at the more sensitive analysis of edges after the preprocessing. Then, predicted masks were utilized for the accurate calculation of wound areas. Eventually, the therapy efficacy-indicator wound areas were thoroughly compared with current well-known tools such as ImageJ and TScratch. Results The average dice similarity coefficient (DSC) scores were obtained as 0.958$$\sim$$ ∼ 0.968 for U-net-based deep learning models. The averaged absolute percentage errors (PE) of predicted wound areas to ground truth were 6.41%, 3.70%, and 3.73%, respectively for U-net, U-net++, and Attention U-net, while ImageJ and TScratch had considerable averaged error rates of 22.59% and 33.88%, respectively. Conclusions Comparative analyses revealed that the developed models outperformed the conventional approaches in terms of analysis time and segmentation sensitivity. The developed models also hold great promise for the prediction of the in vitro wound area, regardless of the therapy-of-interest, cell line, magnification of the microscope, or other application-dependent parameters.

show abstract

A novel image analysis algorithm reveals that media conditioned with chitosan and platelet-rich plasma biomaterial dose dependently increases fibroblast migration in a scratch assay

Cited by 2 publications

References 43 publications

An automated in vitro wound healing microscopy image analysis approach utilizing U-net-based deep learning methodology

An automated in vitro wound healing microscopy image analysis approach utilizing U-net-based deep learning methodology

An automated in vitro wound healing microscopy image analysis approach utilizing U-net-based deep learning methodology

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